CHAPTER 2DOES TQM/CQI WORK IN HEALTH CARE?McLaughlin and Kaluzny, Continuous Quality Improvement in Health Care, 3rd Ed.

MUCH EXPERIENCE, MIXED RESULTS Really not controllable for a controlled trial Many other trends going on Lots of anecdotal evidence In a high variability environment Prevention is more effective, but less

measurable Focus is on customer who is not expert

KEY ISSUE IS THE BUSINESS CASE

WIIFM (what’s in it for me?) Multiple levels of benefit

Customers (patients, payers) Professionals Institutions (business units) Regulators

Whoever pays the piper calls the tune, but there are music critics too.

THE BUSINESS CASE What does the business entity that invests in

the intervention realize in net $ over a reasonable time frame discounted at a reasonable rate to its present value.

Can be “bankable” dollars or a reduction in losses or costs avoided.

Alternative is social case which emphasizes benefits to regardless of who pays or receives them.

HOW WELL DOES TQM/CQI FIT HC? Positives Matches general commitment to scientific methods Fits open access to data – (temporary?) Local reality is the only reality Industrialization is taking place

Can be used as mediating approach to deal with professional autonomy vs. organizational learning

Mobilizes nursing workforce Can provide job enrichment to those involved

HOW WELL DOES TQM/CQI FIT - 2

Positives Intrinsically motivating Capturing and refining intellectual capital Reducing managerial overhead Increasing capacity Encouraging lateral linkages Supporting more open marketplace

HOW WELL DOES TQM/CQI FIT - 3 Positives

Can be used to unfreeze relationships & hierarchies Encourages multidisciplinary approaches Consistent with current concerns about medical errors

NegativesAssociated with industry (suits vs. white coats)May ignore ownership of intellectual capital and

fail to compensate for transfer of it to organizationSlow process, takes input time & calendar timeCan only work on a few projects at any one time

HOW WELL DOES TQM/CQI FIT - 3 Other negatives

Other process changes going on in parallel Does not incorporate external skills except through

benchmarking or a re-engineering approach Contrary to administrative styles of many actors Time value of money may be against you

Resultant About a 50% success rate

INCLUDES SEVERAL ALTERNATIVES

Institutional Including evidence-base practices

Professional including recertification and evidence-based

practices, JCAHO, AAFP CHIT Public policy

IOM studies, AHRQ demonstrations Leapfrog Group, NCQA, think tanks

BIG BANG IN HEALTH CARE QUALITY

IOM Reports To Err is Human

Documented the level and cost of medical error Crossing the Quality Chasm

Recommended approaches to reduce those errors

PROLIFERATING BUT CONVERGING MEASUREMENT AND REPORTING SYSTEMS

HEDIS (NCQA) ORYX (JCAHO) OASIS and MDS (HHS Centers for Medicare and

Medicaid Services (CMS)) Medicare Quality Improvement Organizations

(QIOs) State quality reporting requirements tied to

licensure Baldrige Award process

CHAPTER 3THE OUTCOME MODEL OF QUALITYMcLaughlin and Kaluzny, Continuous Quality Improvement in Health Care, 3rd Ed.

CONCEPTUAL STRUCTURE

Aspects of Care Structure (resource inputs)- Process - Outcome

- Quality Dimensions- Access- Technical Management- Management of Interpersonal Relationships- Continuity of Care

USERS OF OUTCOME MEASURES

Consumers Hospitals Professional Societies Insurance Companies Regulators Employers/business groups/coalitions

A LITTLE COMPARISON IS A DANGEROUS THING Differing risks Differing severity Differing comorbidities Differing populations

Social conditions Living arrangements Ability for self-care

RESPOND BY ADJUSTING PATIENT-LEVEL DATA ON: Risk adjustment to standardize to compare

outcomes to those of a standard population Usually based on diagnoses, age, surgical

procedures Alternative benchmarks

Normative data (clinical trials) Empirical (results of comparable institutions) Institutional (own performance over time)

Table 3.2 Predicted and Actual Mortality and

Readmissions, and Ratios, for Hospital A

Predicted Mortality

Actual Mortali

ty

Ratio (P:A)

Predicted Readmissi

ons

Actual Readmissi

ons

Ratio (P:A)

PneumoniaAll Respiratory Diseases

23.870.2

3587

0.680.81

46.9123.3

42116

1.12*1.06

*indicates statistical significance at 0.001

Table 3.3 Ratios of Predicted to Actual Values, by

Hospital and by Outcome, for Pneumonia and for All Respiratory Diseases in Three Hospitals

Hospital A Hospital B Hospital C

Mortality

PneumoniaAll Respiratory Diseases

0.68*0.81*

1.090.98

1.32*1.03

Readmissions

PneumoniaAll Respiratory Diseases

1.12*1.06

1.01 1.31*

0.990.87

*indicates statistical significance at .001

LIMITATIONS OF OUTCOME MEASURES Derived from discharge abstracts and billing

data. Not on a time line. Patient compliance may be a factor. Data quality and coding may differ across

institutions There is no correlation across quality

measures – need mortality, readmissions, and complication rates.

INSTITUTIONAL RESPONSES

Linder’s classification Status quo – reasonable quality at a reasonable

price – compliance orientation Focus on outliers – reasonable quality at a

premium price (administrative controls) Professional network organizations – cooperation

for improvement between administration and clinicians – high quality at a reasonable price

ENABLERS OF CLINICIAN ADAPTATION

Working alliance with those interested in accountability

Behavioral skills to function in interdisciplinary teams

Understanding of methods and tools to assess quality, their strengths and their weaknesses.